Characterization of tumor heterogeneity by latent haplotypes: a sequential Monte Carlo approach
Tumor samples obtained from a single cancer patient spatially or temporally often consist of varying cell populations, each harboring distinct mutations that uniquely characterize its genome. Thus, in any given samples of a tumor having more than two haplotypes, defined as a scaffold of single nucle...
Main Authors: | Oyetunji E. Ogundijo, Xiaodong Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
PeerJ Inc.
2018-05-01
|
Series: | PeerJ |
Subjects: | |
Online Access: | https://peerj.com/articles/4838.pdf |
Similar Items
-
SeqClone: sequential Monte Carlo based inference of tumor subclones
by: Oyetunji E. Ogundijo, et al.
Published: (2019-01-01) -
On Monte Carlo methods for intractable latent variable models
by: Schmon, S
Published: (2020) -
Bayesian optimization with informative parametric models via sequential Monte Carlo
by: Rafael Oliveira, et al.
Published: (2022-01-01) -
Biological Network Inference With GRASP: A Bayesian Network Structure Learning Method Using Adaptive Sequential Monte Carlo
by: Kaixian Yu, et al.
Published: (2021-11-01) -
Anytime Monte Carlo
by: Lawrence M. Murray, et al.
Published: (2021-01-01)